FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty
LIU Wei1,2,3, HAO Peng1,2, ZHENG Zheng1,2, CAI Kaiyuan1
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Science and Technology on Aircraft Control Laboratory, Beijing 100191, China;
3. High-Tech Institute of Xi'an, Xi'an 710025, China
FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty
LIU Wei1,2,3, HAO Peng1,2, ZHENG Zheng1,2, CAI Kaiyuan1
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Science and Technology on Aircraft Control Laboratory, Beijing 100191, China;
3. High-Tech Institute of Xi'an, Xi'an 710025, China
LIU Wei, HAO Peng, ZHENG Zheng, CAI Kaiyuan. FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty[J]. 机器人, 2013, 35(6): 641-650.DOI: 10.3724/SP.J.1218.2013.00641.
LIU Wei, HAO Peng, ZHENG Zheng, CAI Kaiyuan. FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty. ROBOT, 2013, 35(6): 641-650. DOI: 10.3724/SP.J.1218.2013.00641.
摘要
This paper presents an FBCRI (feedback based compositional rule of inference) based novel path planning method to satisfy the requirements of real-time navigation, smoothness optimization and probabilistic obstacle avoidance. With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges, the method infers behavior weights using fuzzy reasoning embedded with feedback, and then coordinates the behaviors to generate new reference waypoints. In view of the deterministic decisions and the uncertain states of a UAV (unmanned air vehicle), chance constraints are adopted to probabilistically guarantee the UAV's safety at a required level. Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments, as well as highly improve the path quality with respect to smoothness and probabilistic safety.
This paper presents an FBCRI (feedback based compositional rule of inference) based novel path planning method to satisfy the requirements of real-time navigation, smoothness optimization and probabilistic obstacle avoidance. With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges, the method infers behavior weights using fuzzy reasoning embedded with feedback, and then coordinates the behaviors to generate new reference waypoints. In view of the deterministic decisions and the uncertain states of a UAV (unmanned air vehicle), chance constraints are adopted to probabilistically guarantee the UAV's safety at a required level. Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments, as well as highly improve the path quality with respect to smoothness and probabilistic safety.
Suppoted by: National Nature Science Foundation of China (60904066)
通讯作者:
LIU Wei
E-mail: everwl@gmail.com
作者简介: LIU Wei（1981- ），male，Ph.D. Candidate，Assistant professor. Research interests include motion planning，flight task decision and optimization. HAO Peng（1986- ），male，Master. Research interests include software fault localization，decision and optimization. ZHENG Zheng（1980- ），male，Ph.D.，Associate professor. Research interests include flight task planning and software fault localization.
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